UAV blade machining accuracy measurement method, equipment and medium

Abstract

The present invention discloses a method, device, and medium for measuring the machining accuracy of drone blades. The method comprises: obtaining point cloud data of the drone blade surface through double-anisotropic line structured light scanning technology; performing non-uniform sampling on the obtained point cloud data based on the micro-local density and curvature information of the point cloud to extract key feature points; constructing a three-dimensional parameter fitting model of the drone blade based on the extracted key feature points to obtain the measurement parameters of the drone blade; performing non-uniform sampling on the three-dimensional model of the drone blade to generate a standard point cloud, and constructing a corresponding standard three-dimensional parameter fitting model to obtain standard parameters; and comparing the obtained measurement parameters with the standard parameters to evaluate the machining accuracy of the drone blade. The present invention obtains high-precision point cloud data of the drone blade surface through double-anisotropic line structured light scanning technology, and uses the three-dimensional parameter fitting model to accurately measure and evaluate the machining accuracy of the drone blade.

Description

Technical Field

[0001] The present invention belongs to the technical field of blade processing accuracy measurement, and specifically relates to a method for measuring the processing accuracy of unmanned aerial vehicle blades based on three-dimensional parameter fitting of surface point clouds, a computer device, and a computer-readable storage medium. Background Art

[0002] With the rapid development of drone technology, the machining accuracy of drone blades, as one of their core components, is crucial to flight performance and stability. Traditional blade machining accuracy measurement methods rely heavily on contact measurement tools, which suffer from low accuracy, complex operation, and easy damage to the object being measured. In recent years, the acquisition of high-precision surface point cloud data based on non-contact scanning technologies, such as structured light scanning, has gradually become a research direction for blade machining accuracy measurement. However, existing point cloud data processing methods still suffer from problems such as uneven sampling and insufficient fitting accuracy, and are unable to accurately reflect the surface characteristics and machining errors of the blades.

[0003] Currently, there are two core problems in the UAV blade machining accuracy measurement process. First, the accuracy of point cloud acquisition and modeling is insufficient. Due to the single scanning path or occlusion interference, traditional non-contact measurement methods often find it difficult to fully obtain high-quality point cloud data of the complex curved surface structure of the blade, resulting in low accuracy in subsequent three-dimensional modeling and parameter extraction, and it is difficult to truly reflect the actual machining status. Second, the processing flow is poorly robust and adaptable. Due to the complex free-form surface structure and local detail changes on the blade surface, the traditional point cloud processing process generally lacks adaptability when facing different types of blades. It is easily disturbed by factors such as noise and uneven density, affecting the reliability and stability of fitting modeling and machining deviation evaluation. Therefore, there is an urgent need for a high-precision and fast blade machining accuracy measurement solution. Summary of the Invention

[0004] In response to the above-mentioned defects in the prior art, the present invention provides a method, equipment and medium for measuring the processing accuracy of UAV blades. It obtains high-precision point cloud data of the surface of UAV blades through double-order anisotropic line structured light scanning technology, and uses a three-dimensional parameter fitting model to accurately measure and evaluate the processing accuracy of UAV blades.

[0005] The first aspect of the present invention provides a method for measuring the machining accuracy of UAV blades based on three-dimensional parameter fitting of surface point clouds, the method comprising:

[0006] The point cloud data of the UAV blade surface is obtained through double-directional structured light scanning technology;

[0007] Based on the micro-local density and curvature information of the point cloud, the obtained point cloud data is non-uniformly sampled to extract key feature points;

[0008] Based on the extracted key feature points, a three-dimensional parameter fitting model of the UAV blade is constructed to obtain the measured parameters of the UAV blade;

[0009] Perform non-uniform sampling on the three-dimensional model of the UAV blade to generate a standard point cloud, and construct the corresponding standard three-dimensional parameter fitting model to obtain the standard parameters;

[0010] The measured parameters were compared with the standard parameters to evaluate the machining accuracy of the UAV blades.

[0011] In the above solution, double-directional structured light scanning technology is used to obtain point cloud data of the drone blade surface, including:

[0012] Fix the drone blades on a six-degree-of-freedom mechanical motion platform and adjust their posture so that their surfaces are within the field of view of the structured light scanning device;

[0013] Using double-directional line structured light scanning technology, line structured light stripe patterns are projected onto the blade surface from two different directions in sequence;

[0014] Use the camera to collect structured light stripe images and convert them into three-dimensional point cloud data in the corresponding direction;

[0015] The three-dimensional point cloud data obtained from two directions are registered and fused to obtain the three-dimensional point cloud data of the blade surface.

[0016] In the above scheme, the dual anisotropic line structured light scanning technology refers to performing line structured light scanning on the drone blades in directions parallel to the X-axis and Y-axis respectively. The scanning process includes:

[0017] Perform line structured light scanning parallel to the X-axis to obtain the first set of point cloud data;

[0018] Perform line structured light scanning parallel to the Y-axis to obtain a second set of point cloud data;

[0019] The first set of point cloud data is registered and superimposed with the second set of point cloud data to obtain complete point cloud data.

[0020] In the above solution, the method further includes:

[0021] The point cloud data of the UAV blade surface obtained is preliminarily denoised to remove isolated points and abnormal points.

[0022] In the above scheme, based on the micro-local density and curvature information of the point cloud, the obtained point cloud data is non-uniformly sampled to extract key feature points, including:

[0023] Calculate the point cloud bounding box and divide it into multiple regular grid units at fixed intervals along the X, Y, and Z axes to divide the point cloud data of the drone blade surface into multiple point cloud blocks;

[0024] Calculate the center point of each point cloud block, which is the mean point of the three-dimensional coordinates of all points in the point cloud block;

[0025] Calculate the Euclidean distance between the center points of two point cloud blocks , and calculate the global average ;in, Indicates the i Point cloud blocks and j The Euclidean distance between the center points of point cloud blocks, N represents the total number of point cloud blocks, Indicates the total number of pairs of point cloud blocks;

[0026] For each point in the point cloud block, the eigenvalue is obtained by covariance matrix decomposition, and the curvature of each point is calculated based on the eigenvalue. Then, the curvature of all points in each point cloud block is counted, and the maximum curvature of each point cloud block is recorded. ;

[0027] Set the curvature threshold T and define the eigenvalue ,in is the weight coefficient, the density value , ; If the eigenvalue corresponding to a certain point cloud block satisfy , then the point cloud block is determined to be a feature area, and the point with the largest curvature in the point cloud block or the point close to the center of the point cloud block is selected as the sampling point;

[0028] The sampling points extracted from all point cloud blocks are merged to form the global key feature point set Q.

[0029] In the above scheme, a three-dimensional parameter fitting model of the UAV blade is constructed based on the extracted key feature points to obtain the measured parameters of the UAV blade, including:

[0030] The extracted key feature points are used as input data to describe the overall spatial shape of the blade through parameterized coordinate component equations. The specific equation is:

[0031]

[0032] in, 、 and is the coordinate of the key feature point, R is the radial radius of any section of the blade, is the rotation angle parameter, k is the control blade surface torsion gradient, is the initial twist angle of the blade root, is the torsion gradient coefficient, is the maximum radius of the blade, is the normalized radial position;

[0033] Traverse the key feature points, find the k and R corresponding to all key feature points, and substitute them , calculate and , as the measurement parameter of the UAV blade.

[0034] In the above scheme, the three-dimensional model of the UAV blade is non-uniformly sampled to generate a standard point cloud, and the corresponding standard three-dimensional parameter fitting model is constructed to obtain standard parameters, including:

[0035] Obtain the CAD model of the UAV blade, and obtain the standard point cloud data of the UAV blade surface based on the surface morphology change characteristics of the UAV blade;

[0036] Based on the micro-local density and curvature information of the point cloud, the standard point cloud data of the UAV blade surface is non-uniformly sampled to extract standard key feature points;

[0037] Based on the extracted standard key feature points, a three-dimensional parameter fitting model of the UAV blade is constructed to obtain the standard parameters of the UAV blade. and ,in is the standard blade root initial twist angle, is the standard torsion gradient coefficient.

[0038] In the above scheme, the measured parameters are compared with the standard parameters to evaluate the machining accuracy of the UAV blades, including:

[0039] Calculate measurement parameters and Corresponding standard parameters and If the two differences are each less than a preset threshold, the drone blade is judged to be qualified, otherwise the drone blade is judged to be unqualified.

[0040] According to a second aspect of the present invention, a computer device is provided, comprising: a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the programs or instructions are executed by the processor, the steps of the method for measuring the machining accuracy of UAV blades described in any one of the first aspects are implemented.

[0041] According to a third aspect of the present invention, a computer-readable storage medium is provided, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the method for measuring the machining accuracy of UAV blades described in any one of the first aspects are implemented.

[0042] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0043] The present invention provides a method, equipment and medium for measuring the machining accuracy of UAV blades based on three-dimensional parameter fitting of surface point clouds. The method can obtain blade surface point cloud data with high precision through double-order anisotropic line structured light scanning technology, and accurately measure the geometric parameters of the actual processed blades through non-uniform sampling and three-dimensional parameter fitting. Finally, the parameters are compared with standard parameters to achieve an effective evaluation of the machining accuracy of UAV blades. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A flow chart of a method for measuring the machining accuracy of UAV blades based on three-dimensional parameter fitting of surface point clouds provided in an embodiment of the present application;

[0045] Figure 2 A scanning point cloud image of a drone blade provided in an embodiment of the present application;

[0046] Figure 3 In a parameterized coordinate component equation provided in an embodiment of the present application, R and Schematic diagram;

[0047] Figure 4 A schematic diagram of the maximum radius of a drone blade provided in an embodiment of the present application;

[0048] Figure 5 A schematic diagram of the hardware structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. Based on the embodiments provided in this application, all other embodiments obtained by those of ordinary skill in the art without making creative work are within the scope of protection of the present invention.

[0050] Obviously, the drawings described below are merely examples or embodiments of the present application. Those skilled in the art can, without inventive effort, apply the present application to other similar scenarios based on these drawings. Furthermore, it is also understood that, although the effort involved in such a development process may be complex and lengthy, for those skilled in the art related to the content disclosed in this application, changes in design, manufacturing, or production based on the technical content disclosed in this application are merely conventional technical means and should not be construed as an insufficiency of the content disclosed in this application.

[0051] References to "embodiments" in this application mean that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it refer to independent or alternative embodiments that are mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described in this application may be combined with other embodiments unless there is a conflict.

[0052] Unless otherwise defined, technical or scientific terms used herein shall have the ordinary meaning as understood by persons of ordinary skill in the art to which this application belongs. The terms "a," "an," "an," "the," and similar expressions used herein do not denote quantitative limitations and may refer to either the singular or the plural. The terms "comprise," "include," "have," and any variations thereof, used herein, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or modules (units) is not limited to the listed steps or units but may also include steps or units not listed, or may include other steps or units inherent to the process, method, product, or apparatus. The terms "connected," "connected," "coupled," and similar expressions used herein are not limited to physical or mechanical connections but may include electrical connections, whether direct or indirect. As used herein, "plurality" means two or more. "And / or" describes an association between associated objects, indicating that three possible relationships exist. For example, "A and / or B" may mean: A exists alone; A and B exist simultaneously; or B exists alone. The character " / " generally indicates that the objects before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific order for the objects.

[0053] The present application provides a method for measuring the machining accuracy of UAV blades based on three-dimensional parameter fitting of surface point clouds, a computer device, and a computer-readable storage medium. The method comprises the following steps: first, obtaining high-precision point cloud data of the surface of the UAV blade using double-order anisotropic line structured light scanning technology; then, performing non-uniform sampling on the point cloud in combination with micro-local density and curvature information to extract key feature points; constructing a three-dimensional parameter fitting model of the blade surface area based on the extracted feature points to obtain the measurement parameters of the UAV blade; further, performing non-uniform sampling on the CAD model of the UAV blade to generate a standard point cloud, and constructing a corresponding standard three-dimensional parameter fitting model to extract standard parameters; finally, by comparing the measured parameters with the standard parameters, an effective evaluation of the machining accuracy of the UAV blade is achieved.

[0054] like Figure 1As shown, the method for measuring the machining accuracy of UAV blades based on three-dimensional parameter fitting of surface point clouds of the present application includes the following steps:

[0055] S101, using double-directional structured light scanning technology to obtain high-precision point cloud data on the surface of the UAV blade;

[0056] S102, based on the micro-local density and curvature information of the point cloud, performing non-uniform sampling on the obtained point cloud data to extract key feature points;

[0057] S103, constructing a three-dimensional parameter fitting model of the UAV blade based on the extracted feature points, and obtaining measurement parameters of the UAV blade;

[0058] S104, performing non-uniform sampling on the CAD model of the UAV blade to generate a standard point cloud, and constructing a corresponding standard three-dimensional parameter fitting model to obtain standard parameters;

[0059] S105 : Compare the measurement parameters obtained in step S103 with the standard parameters obtained in step S104 to evaluate the machining accuracy of the UAV blade.

[0060] In some embodiments, the specific method of step S101 is as follows: fix the drone blade on a six-degree-of-freedom mechanical motion platform and adjust its posture so that its surface is within the field of view of the structured light scanning device; use double-direction line structured light scanning technology to project line structured light stripe patterns onto the blade surface from two different directions in sequence; the camera collects the structured light stripe image and converts it into three-dimensional point cloud data in that direction; align and fuse the point cloud data obtained in the two directions to obtain three-dimensional point cloud data of the blade surface, such as Figure 2 shown.

[0061] Among them, double anisotropic line structured light scanning refers to line structured light scanning of the drone blades from directions parallel to the X-axis and Y-axis respectively. The scanning process includes:

[0062] (1) Perform line structured light scanning parallel to the X-axis to obtain the first set of point cloud data;

[0063] (2) Perform line structured light scanning parallel to the Y-axis to obtain the second set of point cloud data;

[0064] (3) The first set of point cloud data is registered and superimposed with the second set of point cloud data to obtain complete point cloud data.

[0065] In this embodiment, the drone blades are placed horizontally, and the X-axis is the length direction of the blades.

[0066] In some embodiments, the specific method of step S102 is: performing preliminary denoising on the point cloud data obtained in step S101 to remove isolated points and abnormal points; dividing the denoised point cloud into non-overlapping sub-region blocks; for each sub-region point cloud, calculate its geometric center point, which is the three-dimensional coordinate mean point of all points in the point cloud block; between every two sub-region blocks, calculate the Euclidean distance between their respective center points, and take the average of the pairwise distances between all blocks as the overall inter-block average distance; in each point cloud block, use its center point as a reference and divide the point cloud block into four regions along the horizontal direction of 0°, 90°, 180° and 270° respectively. In each region, count the curvature of all points and record the maximum value, and finally take the maximum curvature in the four regions; then perform non-uniform sampling, which is based on the micro-local density and curvature calculation of point cloud data, and give priority to sampling in areas with large local curvature and significant changes, that is, calculate the eigenvalue based on the average distance between blocks and the maximum curvature of each point cloud block, and set a threshold T. If the eigenvalue is greater than the threshold T, the point with the largest curvature in the point cloud block or the point close to the center of the point cloud block is used as the sampling point; merge the sampling points extracted from all blocks to form a global key feature point set Q.

[0067] In some embodiments, the specific method of step S103 is: using the key feature point set Q extracted in step S102 as input data, and describing the overall spatial shape of the blade by parameterized coordinate component equations, the specific equation is:

[0068]

[0069] in, 、 and is the coordinate of the key feature point, R is the radial radius of any section of the blade, is the rotation angle parameter, k is the control blade surface torsion gradient, is the initial twist angle of the blade root, is the torsion gradient coefficient, is the maximum radius of the blade, which is the straight-line distance from the blade's center of rotation to its outermost edge. is the normalized radial position.

[0070] For each key feature point, the corresponding k and R can be calculated through its three-dimensional coordinates, and the k and R corresponding to all key feature points can be substituted into , we can calculate and , calculated and As the measurement parameters of the drone blades, the calculation methods include but are not limited to the least squares method.

[0071] In some embodiments, the specific method of step S104 is: import the CAD model data of the drone blade, pre-process it, remove invalid geometric elements, unify the coordinate system and normal direction, then use the non-uniform sampling in step S102 to extract key feature points, and then use the parameterized coordinate component equation in step S103 to describe the spatial form of the blade CAD model to obtain the corresponding and .

[0072] In this step, the CAD model of the UAV blade is non-uniformly sampled. The sampling process is based on the geometric features of the CAD model. The method for extracting feature points adopts the method in step S102, and the method for constructing a three-dimensional parameter fitting model corresponding to the CAD model is consistent with the method in step S103.

[0073] In some embodiments, the specific method of step S105 is: calculating the error between the actual measurement parameter and the standard parameter, and evaluating the processing accuracy through error analysis. That is:

[0074] Get the measurement parameters obtained in step S103 and At the same time, the standard parameters corresponding to the constructed standard three-dimensional parameter fitting model are obtained from step S4 as a comparison benchmark. The deviation values ​​are calculated respectively, namely:

[0075]

[0076]

[0077] in, and Represents parameters respectively and The deviation, and Indicates the parameters actually measured, and Indicates the standard parameters of corresponding points obtained from the CAD model.

[0078] Set two thresholds T1 and T2. If both parameters meet the error and , the blade is judged to be qualified; otherwise it is judged to be unqualified.

[0079] Specifically, the method for measuring the machining accuracy of UAV blades based on three-dimensional parameter fitting of surface point clouds in the embodiment of the present application is as follows: Figure 1 As shown, the following steps are included:

[0080] S101. Fix the drone blade on a six-degree-of-freedom mechanical motion platform and adjust its posture so that its surface is within the field of view of the structured light scanning device; use double-direction line structured light scanning technology to project line structured light stripe patterns onto the blade surface from two different directions in sequence; the camera collects the structured light stripe image and converts it into three-dimensional point cloud data in that direction; align and fuse the point cloud data obtained from the two directions to obtain three-dimensional point cloud data of the blade surface, such as Figure 2 Among them, the double anisotropic line structured light scanning refers to performing line structured light scanning on the drone blades in directions parallel to the X-axis and the Y-axis respectively. The scanning process includes:

[0081] (a) Scan the line structured light parallel to the X-axis to obtain the first set of point cloud data;

[0082] (b) Perform line structured light scanning parallel to the Y-axis to obtain the second set of point cloud data;

[0083] (c) After performing ICP registration on the first set of point cloud data and the second set of point cloud data, the complete point cloud data of the UAV blade is obtained.

[0084] S102: Perform preliminary denoising on the blade point cloud data obtained in step S101 to remove isolated points and abnormal points, and then start non-uniform sampling. The specific steps are as follows:

[0085] Step 1: Calculate the bounding box range of the point cloud and divide it into regular grid units of 3×3 mm at a fixed interval along the X, Y, and Z axes to form a 3D point cloud spatial block set;

[0086] Step 2: Calculate the coordinates of the geometric center point in each grid cell;

[0087] Step 3: Traverse all point cloud block combinations and calculate the Euclidean distance between each center point , find the global average: , where N is the total number of blocks;

[0088] Step 4: For each point in the block, obtain the eigenvalue by decomposing the covariance matrix The curvature calculation formula is , then take the center of the block as the origin, along Divide the area into four directions, count the curvature of all points in each area, and record the maximum curvature in the four areas ;

[0089] Step 5: Set the curvature threshold T = 0.05 and define the eigenvalue ,in is the weight coefficient, the density value , the smaller L is, the larger R is, , if satisfied , then the block is determined to be a feature area, and the point with the largest curvature or the point close to the center of the block is selected as the sampling point; the sampling points extracted from all blocks are merged to form the global key point set Q.

[0090] S103, the key point set extracted in step S102 As input data, the overall spatial shape of the blade is described by parameterized coordinate component equations, such as Figure 3 and Figure 4 As shown, the specific equation is:

[0091]

[0092] Where R is the radial radius of any section of the blade, is the rotation angle parameter, k is the control blade surface torsion gradient, is the initial twist angle of the blade root, is the torsion gradient coefficient, is the maximum radius of the blade, is the normalized radial position.

[0093] Traverse the surface torsional gradient k of each control blade in the key feature point set and the radial radius R at any section of the blade, substitute it into the above equation, and fit the corresponding and .

[0094] S104: Perform non-uniform sampling on the CAD model of the drone blade to generate a standard point cloud, and construct a corresponding standard three-dimensional parameter fitting model to obtain standard parameters. The specific steps are as follows:

[0095] Step 1: Import the blade CAD model and obtain representative standard point cloud data based on the blade surface morphology characteristics;

[0096] Step 2: Using the same non-uniform point cloud sampling strategy as in step S102, a point set is selected on the surface of the CAD model to form a standard point cloud dataset with structural representativeness;

[0097] Step 3: Preprocess the sampled point cloud to remove abnormal points and duplicate points;

[0098] Step 4: Based on the micro-local density and curvature information of the point cloud, a set of key feature points is extracted from the standard point cloud. The method used to extract feature points is the same as that in step S102.

[0099] Step 5: Based on the extracted key feature points, a standard three-dimensional parameterized model is constructed using the same method as in step S103. Specifically, the following parameterized equation is used:

[0100]

[0101] Where R is the radial radius at any section of the blade CAD model, is the CAD model rotation angle parameter, k is the control blade CAD model surface torsion gradient, is the initial torsion angle of the blade root of the CAD model, is the CAD model torsion gradient coefficient, is the maximum radius of the blade CAD model, is the normalized radial position.

[0102] Similarly, we traverse the surface torsional gradient k of each control blade and the radial radius R at any section of the blade in the key feature point set, substitute them into the above equation, and fit the corresponding and .

[0103] S105. Convert the measurement parameters obtained in step S103 and the standard parameters obtained in step S104 into a unified format to ensure that the two sets of parameters are consistent in terms of domain, variable form, unit system, etc.; calculate the relative error or absolute error between the measurement parameters and the standard parameters using the following formula:

[0104]

[0105]

[0106] in, and Represents parameters respectively and The deviation, and Indicates the parameters actually measured, and Indicates the standard parameters of corresponding points obtained from the CAD model.

[0107] Set two thresholds T1 and T2. If both parameters meet the error and , the blade is judged to be qualified; otherwise it is judged to be unqualified.

[0108] Of course, you can also choose any parameter to calculate the deviation rate, that is, subtract the deviation from the standard parameter to get the processing accuracy.

[0109] It should be noted that the steps shown in the above process or the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0110] In addition, combined Figure 1 The method for measuring the machining accuracy of the drone blades described in the embodiment of the present application can be implemented by a computer device. Figure 5 Schematic diagram of the hardware structure of the computer device of the embodiment of the present application. Figure 5 As shown, the device may include a processor 301 and a memory 302 storing computer program instructions.

[0111] Specifically, the processor 301 may include a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0112] Memory 302 may include a large-capacity memory for data or instructions. By way of example, and not limitation, memory 302 may include a hard disk drive (HDD), a floppy disk drive, a solid-state drive (SSD), flash memory, an optical disk, a magneto-optical disk, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 302 may include removable or non-removable (or fixed) media. Where appropriate, memory 302 may be internal or external to the data processing device. In certain embodiments, memory 302 is non-volatile memory. In certain embodiments, memory 302 includes read-only memory (ROM) and random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM) or a flash memory (FLASH), or a combination of two or more of these. Under appropriate circumstances, the RAM can be a static random access memory (SRAM) or a dynamic random access memory (DRAM), where the DRAM can be a fast page mode dynamic random access memory (FPMDRAM), an extended data out dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0113] The memory 302 may be used to store or cache various data files that need to be processed and / or used for communication, as well as possible computer program instructions executed by the processor 301 .

[0114] The processor 301 reads and executes computer program instructions stored in the memory 302 to implement any one of the UAV blade machining accuracy measurement methods in the above embodiments.

[0115] In some embodiments, the point cloud generation device may further include a communication interface 303 and a bus 300. Figure 5 As shown, the processor 301 , the memory 302 , and the communication interface 303 are connected via a bus 300 and communicate with each other.

[0116] The communication interface 303 is used to implement communication between the various modules, devices, units, and / or devices in the embodiments of the present application. The communication interface 303 can also implement data communication with other components such as: external devices, image / data acquisition equipment, databases, external storage, and image / data processing workstations.

[0117] Bus 300 , which includes hardware, software, or both, couples the components of the point cloud generation device to one another. Bus 300 includes, but is not limited to, at least one of the following: a data bus, an address bus, a control bus, an expansion bus, and a local bus. By way of example, and not limitation, bus 300 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Bus 300 may include one or more buses, where appropriate. Although embodiments herein describe and illustrate a particular bus, this application contemplates any suitable bus or interconnect.

[0118] The computer device can execute the UAV blade machining accuracy measurement method in the embodiment of the present application, thereby realizing the combination Figure 1 The method for measuring the machining accuracy of UAV blades is described.

[0119] In addition, in conjunction with the UAV blade machining accuracy measurement method described in the above embodiments, embodiments of the present application may provide a computer-readable storage medium for implementation. The computer-readable storage medium stores computer program instructions; when executed by a processor, the computer program instructions implement any of the UAV blade machining accuracy measurement methods described in the above embodiments.

[0120] It should be noted that the various technical features of the above-described embodiments can be combined in any manner. To simplify the description, not all possible combinations of the various technical features in the above-described embodiments are described. However, as long as there are no contradictions in the combination of these technical features, they should be considered to be within the scope of this specification. In addition, according to the needs of implementation, the various steps / components described in this application can be split into more steps / components, and two or more steps / components or partial operations of steps / components can be combined into new steps / components to achieve the purpose of the present invention.

[0121] Those skilled in the art will readily understand that the above-described embodiments merely represent several implementation methods of the present application, and their descriptions are relatively specific and detailed, but should not be construed as limiting the scope of the invention. It should be noted that a person of ordinary skill in the art may make several variations and improvements without departing from the concept of the present application, and these variations and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be based on the appended claims.

Claims

1. A method for measuring the machining accuracy of UAV blades based on three-dimensional parameter fitting of surface point clouds, characterized in that: The method includes: The point cloud data of the UAV blade surface is obtained through double-directional structured light scanning technology; Based on the micro-local density and curvature information of the point cloud, the obtained point cloud data is non-uniformly sampled to extract key feature points; Based on the extracted key feature points, a three-dimensional parameter fitting model of the UAV blade is constructed to obtain the measured parameters of the UAV blade; Perform non-uniform sampling on the three-dimensional model of the UAV blade to generate a standard point cloud, and construct the corresponding standard three-dimensional parameter fitting model to obtain the standard parameters; Compare the measured parameters with the standard parameters to evaluate the machining accuracy of the UAV blades; Among them, the double-direction line structured light scanning technology refers to the line structured light scanning of the drone blades from the directions parallel to the X-axis and Y-axis respectively. The scanning process includes: Perform line structured light scanning parallel to the X-axis to obtain the first set of point cloud data; Perform line structured light scanning parallel to the Y-axis to obtain a second set of point cloud data; Register and overlay the first set of point cloud data with the second set of point cloud data to obtain complete point cloud data; Based on the micro-local density and curvature information of the point cloud, the obtained point cloud data is non-uniformly sampled to extract key feature points, including: Calculate the point cloud bounding box and divide it into multiple regular grid units at fixed intervals along the X, Y, and Z axes to divide the point cloud data of the drone blade surface into multiple point cloud blocks; Calculate the center point of each point cloud block, which is the mean point of the three-dimensional coordinates of all points in the point cloud block; Calculate the Euclidean distance between the center points of two point cloud blocks , and calculate the global average ;in, Indicates the i Point cloud blocks and j The Euclidean distance between the center points of point cloud blocks, N represents the total number of point cloud blocks, Indicates the total number of pairs of point cloud blocks; For each point in the point cloud block, the eigenvalue is obtained by covariance matrix decomposition, and the curvature of each point is calculated based on the eigenvalue. Then, the curvature of all points in each point cloud block is counted, and the maximum curvature of each point cloud block is recorded. ; Set the curvature threshold T and define the eigenvalue ,in is the weight coefficient, the density value , ; If the eigenvalue corresponding to a certain point cloud block satisfy , then the point cloud block is determined to be a feature area, and the point with the largest curvature in the point cloud block or the point close to the center of the point cloud block is selected as the sampling point; The sampling points extracted from all point cloud blocks are merged to form the global key feature point set Q.

2. The method for measuring the machining accuracy of UAV blades based on three-dimensional parameter fitting of surface point clouds according to claim 1 is characterized in that: The point cloud data of the drone blade surface is obtained through double-directional structured light scanning technology, including: Fix the drone blades on a six-degree-of-freedom mechanical motion platform and adjust their posture so that their surfaces are within the field of view of the structured light scanning device; Using double-directional line structured light scanning technology, line structured light stripe patterns are projected onto the blade surface from two different directions in sequence; Use the camera to collect structured light stripe images and convert them into three-dimensional point cloud data in the corresponding direction; The three-dimensional point cloud data obtained from two directions are registered and fused to obtain the three-dimensional point cloud data of the blade surface.

3. The method for measuring the machining accuracy of UAV blades based on three-dimensional parameter fitting of surface point clouds according to claim 1, characterized in that: The method further includes: The point cloud data of the UAV blade surface obtained is preliminarily denoised to remove isolated points and abnormal points.

4. The method for measuring the machining accuracy of UAV blades based on three-dimensional parameter fitting of surface point clouds according to claim 1, characterized in that: Based on the extracted key feature points, a 3D parameter fitting model of the UAV blade is constructed to obtain the measured parameters of the UAV blade, including: The extracted key feature points are used as input data to describe the overall spatial shape of the blade through parameterized coordinate component equations. The specific equation is: in, 、 and is the coordinate of the key feature point, R is the radial radius of any section of the blade, is the rotation angle parameter, k is the control blade surface torsion gradient, is the initial twist angle of the blade root, is the torsion gradient coefficient, is the maximum radius of the blade, is the normalized radial position; Traverse the key feature points, find the k and R corresponding to all key feature points, and substitute them , calculate and , as the measurement parameter of the UAV blade.

5. The method for measuring the machining accuracy of UAV blades based on three-dimensional parameter fitting of surface point clouds according to claim 4, characterized in that: Perform non-uniform sampling on the 3D model of the UAV blade to generate a standard point cloud, and construct the corresponding standard 3D parameter fitting model to obtain the standard parameters, including: Obtain the CAD model of the UAV blade, and obtain the standard point cloud data of the UAV blade surface based on the surface morphology change characteristics of the UAV blade; Based on the micro-local density and curvature information of the point cloud, the standard point cloud data of the UAV blade surface is non-uniformly sampled to extract standard key feature points; Based on the extracted standard key feature points, a three-dimensional parameter fitting model of the UAV blade is constructed to obtain the standard parameters of the UAV blade. and ,in is the standard blade root initial twist angle, is the standard torsion gradient coefficient.

6. The method for measuring the machining accuracy of UAV blades based on three-dimensional parameter fitting of surface point clouds according to claim 5, characterized in that: Compare the measured parameters with the standard parameters to evaluate the machining accuracy of the UAV blades, including: Calculate measurement parameters and Corresponding standard parameters and If the two differences are each less than a preset threshold, the drone blade is judged to be qualified, otherwise the drone blade is judged to be unqualified.

7. A computer device, characterized in that: include: A processor and a memory, the memory storing programs or instructions that can be run on the processor, and the programs or instructions, when executed by the processor, implementing the steps of the method for measuring the machining accuracy of UAV blades as described in any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that A program or instruction is stored thereon, and when the program or instruction is executed by the processor, the steps of the method for measuring the machining accuracy of UAV blades described in any one of claims 1 to 6 are implemented.

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